Team Lapiyano – Motus x UJ Hackathon 2025

Welcome to Team Lapiyano's submission for the Motus & University of Johannesburg Hackathon 2025.

Our project tackles the challenge of predicting whether a car lead results in a sale, using machine learning techniques with a focus on real-world impact and data-driven insights.

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🔍 Problem Statement

Predict the likelihood of a lead converting into a sale based on attributes provided in the Motus dataset. The solution aims to assist dealerships in optimizing their sales pipeline and focusing on high-potential leads.

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📁 Project Structure

File / Folder	Description
TeamLapiyano.ipynb	Core notebook with full preprocessing, modeling, and evaluation pipeline
TeamLapiyano_commented.ipynb	Same notebook with detailed inline comments explaining every step
Final Evaluation Standings/Results.pdf	📊 Final performance evaluation provided by organizers

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📊 Approach Summary

• Model Used: XGBoost Classifier
• Preprocessing:
  • Handling missing values with SimpleImputer
  • Encoding categorical features
  • Feature selection based on domain insights
• Balancing Strategy:
  • Applied SMOTE (Synthetic Minority Oversampling) for class imbalance
• Evaluation Metrics:
  • Precision-Recall AUC
  • Confusion Matrix
  • ROC Curve

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📦 Requirements

To run this notebook:

pip install -r requirements.txt

Minimum packages:

• xgboost
• pandas
• numpy
• scikit-learn
• imblearn
• matplotlib
• seaborn (optional)

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📄 Final Evaluation

You can find the official evaluation results in:

📁 Final Evaluation Standings/
   └── 📄 Results.pdf

This file contains performance standings as assessed by the hackathon organizers.

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🚀 Authors

• Team Lapiyano – University of Johannesburg Hackathon 2025

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📬 Contact

For any questions or feedback, feel free to reach out to the team or open an issue.